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  <title>Description of histc_nD</title>
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<h1>histc_nD
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<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Generalized, multidimensional version of normalized histc (histogram count).</strong></div>

<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>function h = histc_nD( I, edges, weightmask ) </strong></div>

<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Generalized, multidimensional version of normalized histc (histogram count).

 Generalized version of normalized histc (histogram count) that allows for weighted
 pixels and also multiple channels.  For example, suppose I is a nx2 array (n samples, 2
 channels). Then histc_nD creates a 2D histogram such that h(q1,q2) contains the weighted
 count of values [v1;v2] in I such that edges1(q1)&lt;=v1&lt;edges1(q1+1) and 
 edges2(q2)&lt;=v2&lt;edges2(q2+1). 

 The histogram edge vectors can be specified in a number of ways.  If edges is a scalar,
 it is treated as a desired number of bins per dimension and a separate edges vector is
 generated for each dimension, for details on how this works see histc_1D.  If edges is a
 vector, than this vector is used as the edges vector along every dimension.  Finally, to
 specify a different set of edges along each dimension use a cell vector of length nd
 where each element is again a scalar or vector. Finally h is normalized so that
 sum(h(:))==1.  

 See histc_1D for more details about edges and nbins. 

 INPUTS
   I           - 2D numeric array [n x nd] 
   edges       - either a scalar, vector, or length n cell vector of scalars and vectors
   weightmask  - [optional] n length vector of weights

 OUTPUTS
   h           - histogram (array of size nbins1xnbins2x...)
 
 EXAMPLE
   G = filter_gauss_nD([1000 1000],[],[],0); G=G(:);
   h=histc_nD( [G G], 25 ); figure(1); im(h); %decreasing vals along diagnol
   h=histc_nD( [G G], 25, G ); figure(2); im(h); %constants along diagnol
   h=histc_nD( [randn(size(G)) G], 5 ); figure(3); im(h); % symmetric about x, dec on y

 DATESTAMP
   29-Sep-2005  2:00pm

 See also <a href="histc_1D.html" class="code" title="function h = histc_1D( I, edges, weightmask )">HISTC_1D</a></pre></div>

<!-- crossreference -->
<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="histc_sift_nD.html" class="code" title="function hs = histc_sift_nD( I, edges, pargmask, weightmask, multch )">histc_sift_nD</a>	Creates a series of locally position dependent histograms.</li></ul>
<!-- crossreference -->



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